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62 S. Balocco et al.
AsshowninFig.4a, the performance of OSRAD is comparable to SRBF when
the noise pattern is small, and progressivelydecreases in the presence of big speckle
patterns. In this last case, as it can be seen in Fig. 4c, OSRAD enlarges the edges.
The same behavior can be observed at different contrasts (
γ
). The performance
decrease of OSRAD filter is probably due to its constant size neighbor support,
while SRBF automatically adapts its spatial support depending on the speckle
pattern. Concluding,SRBF exhibits higher robustness because of its fully automatic
settings of relevant parameters, the speckle noise statistics, and the speckle size.
4.1.2 In Vivo Experiments
Despeckle algorithms are generally used as pre-processing step for automatic
segmentation of US images. The performance of the sole filter can be hardly
validated on in vivo images since reference denoised data is obviously not available.
Instead, the accuracy improvement of a segmentation algorithm is quantified when
the images are denoised.
For this purpose, the best two algorithm (SRBF and OSRAD) listed in Table 1 are
compared on Intravascular Ultrasound (IVUS) images because of their challenging
properties: the anisotropy of the speckle patterns, the presence of artifacts, and the
variable echogenicity of the tissues.
IVUS In Vivo Images
Fifty slices, chosen to represent different vascular structures (plaque and vessel
shape, presence of stent, lumen area, and diameter), were extracted from a data base
of 3,000 in vivo coronary images. The acquisition was performed using an IVUS
Galaxy II System with a catheter Atlantis SR Pro 40 MHz (Boston Scientific). For
each slice the RF signal is available.
From all the 50 slices in polar coordinates different sets of images are computed:
the envelope of the RF signal (data-set A ), the OSRAD filtered envelope (data-set
B), and the SRBF filtered envelope (data-set C). In order to avoid the discontinuity
at 0/360
texture before filtering, copying 30
◦
of the IVUS image, we mirrored the leftmost (0◦) and rightmost (360◦)
◦
of both on the opposite side, thus obtaining
a continuous signal at the boundaries. The catheter ring-down artifact located at
the top of the polar image was suppressed by filling the region with a uniform
color computed as the mean intensity of the underlying stripe, 0.3 mm thick,
adjacent to the catheter area. The same stripe, containing only blood, has been
defined as uniform region required by the OSRAD filter. All the images, were
log compressed and converted to cartesian coordinates prior to segmentation and
visualization.
Ultrasound Despeckle Methods 63
A classic level-set active contour (snake) algorithm5[56], commonly used to
segment US medical images [57], is applied to the three data-sets. The initialization
shape and the level-set smoothness parameter are fixed and tuned to obtain the
best performance on the envelope data-set. The same settings are used when the
snake is applied to the data-sets B and C . The snake propagates from an initial
circular contour until stopping at an energy-stable configuration dependent on the
image gradients. The snake inner area is obtained from the zero level-set shape
at convergence. Ground-truth shapes, indicating the expected segmentation result,
were manually delineated in each of the IVUS frames by two independent experts.
The operator performing the analysis was blinded to the automatic and to the other
manual segmentation results.
Segmentation Comparison
Figure 5 presents the manual delineated border and the results of the automatic
segmentation for seven representative frames of the three data-sets A , B,andC .
Frame #1 illustrates an accurate automatic segmentation obtained in all the three
data-sets. In frame #2, the snake crosses the vessel border between the stent wires
(highly echogenic spots) and spreads in the surrounding tissue. On the contrary,
the snake propagation stops correctly at the lumen frontier in data-sets B and C ,
since both filters enhance the outline of the vessel membrane by smoothing the
noise and by preserving the sharp vessel edges. Similarly in frame #3 the denoising
methods enhance the borders making the segmentation easier. However in this case
(frame #3) the segmentationof data-set B is slightly less accurate, sincethe filtering
method fuses a small catheter ringing artifact with the vessel border, creating an
artificial boundary. The same behavior can be observed in frame #4 where, in dataset B, the noise lying between the catheter artifact and the vessel is not removed
but homogenized.
Frame #5 illustrates that the high amount of noise (data-set A ) may hamper
the snake propagation on the envelope data, while an efficient smoothing promote
an accurate and automatic segmentation (data-sets B and C ). However it is worth
to note that, in data-set B (frame #5), the image filtered with OSRAD is locally
smoothed, but radial ripples are still present since OSRAD has a fixed support. On
the other hand, SRBF better homogenizes uniform areas, since the spatial support
controlled by the function c is wider and has been designed to automatically contain
consecutivescatter peaks. For this reason probably, in frame #6 the snake applied to
B gets trappedin a local minima and stops propagating after 560 iterations, while in
C the snake keeps spreading inside the whole lumen. Finally in frame #7 the snake
spans outside the vessel for data-sets A and B, but not in C . In this frame, the
OSRAD filter enhances the shadow area on the left of the catheter, and creates an
undesired gradient attracting the snake in B, while SRBF homogenizes the shadow
5
http://www.shawnlankton.com/2007/05/active-contours/
64 S. Balocco et al.
Fig. 5 Snake manual (first column) and automatic (second, third, and forth columns) segmentations of seven representative frames from the three data-sets A , B,andC . The last column
illustrates the average distance of each automatic segmentation to the manual segmentations
region with the rest of the lumen in C. In fact, compared to OSRAD which is a zero
mean filter, the SRBF noise model fits better the speckle distribution at low image
intensities and discriminate more accurately between noise and tissues.
Ultrasound Despeckle Methods 65
The last column of Fig. 5 represents the average distance of the automatic
segmentation to the two manual segmentations. In frame #1 the high slope of the
curve indicates that the snake converges faster in filtered data-sets (B and C )since
the noise is accurately removed. In frame #4, a similar behavior can be observed,
but the segmentation of B results as the slowest and the less accurate because of
the wrinkled lumen region. In frame #2, #3, and #5 the segmentation applied to the
non-filtered image diverges, while the two filters show similar convergence speed.
These examples illustrate the interest of using an accurate filtering method before
the segmentation stage. In frame #6 the snake applied to data-set B stops expanding
earlier than data-set C leading to an incorrect vessel segmentation and to a lumen
area under-estimation. Finally, in frame #7 the SRBF filtered the snake applied to
data-set A and B diverges, and only the snake applied to the data-set C converges
to the vessel wall. In all the cases, the segmentation on data-set C is either the
fastest or the only one converging to the vessel boundary, showing the superior
performances of SRBF.
Figures 6 and 7 show a pairwise error comparison for all the frames of the
three data-sets (A , B,andC ). In particular, Fig. 6 illustrates the error between
the automatic and ground-truth border segmentations, while Fig. 7 shows the error
in the lumen area estimations.
In Fig. 6a most of the markers are located below the solid line of unitary
slope, indicating that the average distance between the automatic and the manual
segmentations is smaller in the filtered data-sets (B, C ) thatin the non-filtered(A ).
Figure 6b, shows the superior accuracy obtained by filtering the images with SRBF.
The average distance over all the images of the data-sets A , B,andC with
respect to the manual segmentations are 0.27 ±0.47 mm, 0.15 ±0.24 mm, and
0.12±0.14mm respectively, confirming that the contoursobtained fromthe data-set
C exhibit the lowest segmentation error. The average distance over all the images
of the inter-observer variability is 0.021 mm.
In order to quantify the error in estimating the area for the three data-sets the
Jaccard coefficient [58] defined as the ratio R =
of pixels of the manual segmentation, and
|Φ∩Ψ|
is used, whereΦis the set
|Φ∪Ψ|
Ψ
is the set of pixels resulting from
automatic segmentation [59]. The score R is one when the areas matches exactly
and rapidly decreases when one of the two areas differs from the other.
The performance of the automatic segmentation on the filtered data-sets B and
C , is superior to the non-filtered images A (see Fig. 7a), since R is higher than
the line with unitary slope for most of the images. Analyzing the scatter plot of
Fig. 7b, and comparing directly the performance of the snake on the two denoised
data-sets, it can be noticed that the R-score is similar, but some outliers, representing
unsuccessful segmentations of data-set B, are present.
In more than 50% of the images, the R-score corresponding to OSRAD filter
ranges from 0.12 to 0.33, while for SRBF R varies from 0.06 to 0.24 (Fig. 6b).
Analyzing the scatter plot of Fig. 7b in detail, it can be noticed that in about 50%
of the images, OSRAD R-score ranges from 0.2to0.8, while for SRBF R varies
from 0.6to0.9. The average R-score over all the images of the data-sets A , B and
66 S. Balocco et al.
Fig. 6 Average distance of the automatic segmentation to the two manual segmentations. (a)com-
parison of data-sets B and C vs A ;(b) pairwise comparison of data-sets B vs C
Fig. 7 Area ratio R representing the likeness between the automatically segmented areas. (a)comparison of data-sets B and C vs A ;(b) pairwise comparison of data-sets B and C
C are 0.41 ±0.29, 0.76±0.18 and 0.83±0.09 respectively, showing that the areas
recovered on the data-set C are the most similar to the manually segmented regions.
ANOVA and t-test statistical analysis were performed for both segmentation
distances and R-scores in data-sets A , B,andC . In all cases the scores of datasets B and C are significantly different (p-values ¡1 ×10
−9
for both ANOVA and
t-test) from data-set A . Comparing data-sets B and C , the ANOVA provides p-
values of 0.0032 and 0.0153 respectively for the distances and R-scores; and the
t-test provides p-values of 5.5×10
−4
and 0.0039 respectively for the distances and
R-scores. These tests confirm the statistical significance of the results. The R-score
over all the images of the inter-observer variability is 0.95.
Ultrasound Despeckle Methods 67
Concluding, SRBF outperforms the filtering methods based on zero mean noise
assumptions. Compared to OSRAD, SRBF approach is fully automatic, locally
adaptive, and the number of iterations needed to reach the optimal solution is
smaller. Additionally, SRBF results in a more robust algorithm suitable for a wide
ranges of speckle noise sizes.
5 Conclusion
In this chapter, several speckle denoising filters are proposed and compared.
Particularly one family of filters (anisotropic algorithms) are analyzed both qualitatively and quantitatively on ultrasound data. A series of in silico experiments
has been designed with the aim to compare the performances of the state-of-theart approaches on synthetic images corrupted by a controlled amount of speckle
noise. Additional in vivo experiments has been designed for illustrating the interest
of using an accurate filtering method as pre-processing stage, in order to improve
the performance of the registration and segmentation methods.
Finally the computational cost of most of the algorithm is provided in order to
evaluate the applicability in unsupervised filtering of large amounts of clinical data.
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Balocco Simone Balocco Simone is a
lecturer professor of the Universitat de
Barcelona, and works in the Medical
Imaging group lead by Petia Radeva.
He obtained a PhD degree in Acoustics at the laboratory Creatis, University
Lyon1, Lyon (France) and contemporaneous Ph.D. in Electronic and Telecommunication in MSD Lab, University of
Florence (Italy). His research interests
are: vascular and cardiac tissue modeling, Ultrasound and Magnetic Resonance
signal and image processing and inverse
problem solution.
Carlo Gatta Carlo Gatta obtained the
degree in Electronic Engineering in 2001
from the Universit˜A degli Studi di Brescia (Italy). In 2006 he received the Ph.D.
in Computer Science at the Universit˜A
degli Studi di Milano (Italy) with a thesis on perceptually based color image
processing. In September 2007 he joined
the Computer Vision Center at Universitat Automona de Barcelona (UAB) as
a postdoc researcher working mainly on
medical imaging. He is member of the
Computer Vision Center and the BCN
Perceptual Computing Lab. His main research interests are image processing,
medical imaging, computer vision and
contextual learning.
Ultrasound Despeckle Methods 71
Josepa Mauri Ferr´e Josepa Mauri
received the title of MD in 1982 at Universitat Aut`onoma de Barcelona. In 1992
she received the Laurea summa Cum
Laude in Medicine at the Universitat de
Barcelona. Since 2000, she is the Director of Cardiac Catherization Laboratory
in the Hospital Universitari “Germans
Trias I Pujol de Badalona”. From 2002
to 2005 she was the President of the
Diagnostic Intracoronary Technics/IVUS
Working Group of the Spanish Society
of Cardiology. From 2006 to 2009 she
was the President of the Spanish Working
Group in Cardiac Interventions of the
Spanish Society of Cardiology. Her clin-
ical research areas have been in coronary
angioplasty and Dilated Cardiomyopathy, dilated Cardiomiopathy, coronary angioplasty, stents, endothelial disfunction and IVUS studies. She is currently involved in
international educational projects in interventional cardiology.
Petia Radeva Petia Radeva (PhD 1998,
Universitat Aut`onoma de Barcelona,
Spain) is a senior researcher at UB. She
has more than 150 publications in inter-
national journals and proceedings. Her
present research interests are develop-
ment of learning-based approaches (in
particular statistical methods) for com-
puter vision and medical imaging. She
has led one EU project and several Span-
ish projects. She has 12 patents in the
field of medical imaging. Currently, she
is heading projects in the field of cardiac
imaging and wireless endoscopy in collaboration with Spanish hospitals and international medical imaging companies.